气候变化领域集成服务门户(CCPortal): Interpreting machine learning prediction of fire emissions and comparison with FireMIP process-based models

CCPortal

DOI	10.5194/acp-22-3445-2022
	Interpreting machine learning prediction of fire emissions and comparison with FireMIP process-based models
	Wang, Sally S-C; Qian, Yun; Leung, L. Ruby; Zhang, Yang
发表日期	2022
ISSN	1680-7316
EISSN	1680-7324
起始页码	3445
结束页码	3468
卷号	22 期号:5 页码:24
英文摘要	Annual burned areas in the United States have increased 2-fold during the past decades. With more large fires resulting in more emissions of fine particulate matter, an accurate prediction of fire emissions is critical for quantifying the impacts of fires on air quality, human health, and climate. This study aims to construct a machine learning (ML) model with game-theory interpretation to predict monthly fire emissions over the contiguous US (CONUS) and to understand the controlling factors of fire emissions. The optimized ML model is used to diagnose the process-based models in the Fire Modeling Intercomparison Project (FireMIP) to inform future development. Results show promising performance for the ML model, Community Land Model (CLM), and Joint UK Land Environment Simulator-Interactive Fire And Emission Algorithm For Natural Environments (JULES-INFERNO) in reproducing the spatial distributions, seasonality, and interannual variability of fire emissions over the CONUS. Regional analysis shows that only the ML model and CLM simulate the realistic interannual variability of fire emissions for most of the subregions (r > 0.95 for ML and r = 0.14 similar to 0.70 for CLM), except for Mediterranean California, where all the models perform poorly (r = 0.74 for ML and r < 0.30 for the FireMIP models). Regarding seasonality, most models capture the peak emission in July over the western US. However, all models except for the ML model fail to reproduce the bimodal peaks in July and October over Mediterranean California, which may be explained by the smaller wind speeds of the atmospheric forcing data during Santa Ana wind events and limitations in model parameterizations for capturing the effects of Santa Ana winds on fire activity. Furthermore, most models struggle to capture the spring peak in emissions in the southeastern US, probably due to underrepresentation of human effects and the influences of winter dryness on fires in the models. As for extreme events, both the ML model and CLM successfully reproduce the frequency map of extreme emission occurrence but overestimate the number of months with extremely large fire emissions. Comparing the fire PM2.5 emissions from the ML model with process-based fire models highlights their strengths and uncertainties for regional analysis and prediction and provides useful insights into future directions for model improvements.
学科领域	Environmental Sciences; Meteorology & Atmospheric Sciences
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:000771356600001
来源期刊	ATMOSPHERIC CHEMISTRY AND PHYSICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/273749
作者单位	United States Department of Energy (DOE); Pacific Northwest National Laboratory; Northeastern University
推荐引用方式 GB/T 7714	Wang, Sally S-C,Qian, Yun,Leung, L. Ruby,et al. Interpreting machine learning prediction of fire emissions and comparison with FireMIP process-based models[J],2022,22(5):24.
APA	Wang, Sally S-C,Qian, Yun,Leung, L. Ruby,&Zhang, Yang.(2022).Interpreting machine learning prediction of fire emissions and comparison with FireMIP process-based models.ATMOSPHERIC CHEMISTRY AND PHYSICS,22(5),24.
MLA	Wang, Sally S-C,et al."Interpreting machine learning prediction of fire emissions and comparison with FireMIP process-based models".ATMOSPHERIC CHEMISTRY AND PHYSICS 22.5(2022):24.